class_docstr: Regresses one or more floating point values given a single document.
  
  For a full list of configuration options, see `finetune.config`.
  
  :param config: A config object generated by `finetune.config.get_config` or None (for default config).
  :param \**kwargs: key-value pairs of config items to override.
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docstr: :param X: list or array of text.
  :param Y: floating point targets
  :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number
                     corresponds to the number of training examples provided to each GPU.
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code: def finetune(self, X, Y=None, context=None, **kwargs):
      """
      :param X: list or array of text.
      :param Y: floating point targets
      :param batch_size: integer number of examples per batch. When N_GPUS > 1, this number
                         corresponds to the number of training examples provided to each GPU.
      """
      return super().finetune(X, Y=Y, context=context, **kwargs)
  
file_path: finetune/finetune/target_models/regressor.py
imports: import numpy
  from finetune.base import BaseModel
  from finetune.encoding.target_encoders import RegressionEncoder
  from finetune.nn.target_blocks import regressor
  from finetune.input_pipeline import BasePipeline
label_desc: Use image node_id 6 for calling the Regressor obj's underlying member method code with example usage: obj.finetune(X, Y, context) and returns: super